Benchmarking of a Massively Parallel Hybrid CFD Solver for Ocean Applications

2013 ◽  
Author(s):  
Andrew G. Gerber ◽  
Kevin W. Wilcox ◽  
Jian T. Zhang
Keyword(s):  
High Performance ◽  
Computer Architectures ◽  
Massively Parallel ◽  
Code Design ◽  
Parallel Hybrid ◽  
Computing Industry ◽  
Order Of Magnitude ◽  
Performance Gains ◽  
Performance Computing ◽  
Validation Testing

This paper presents progress on the development of a CFD program called EXN/Aero specifically designed to exploit performance gains from new hybrid multicore-manycore computer architectures. The hybrid multicore-manycore design is outlined along with performance and validation testing on an underwater vehicle and unsteady vortex shedding applications. It is shown that by revisiting CFD code design with a view to a number of important trends in the high performance computing industry, significant order of magnitude gains in computational power can be achieved.

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

Comparing High-Performance Computing Techniques for Modeling Structural Impact on Battery Cells

2014 ◽  
Author(s):  
Mehdi Gilaki ◽  
Ilya Avdeev
Keyword(s):  
Finite Element ◽  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Strain Curve ◽  
Thin Layers ◽  
Massively Parallel ◽  
Structural Impact ◽  
Performance Computing ◽  
Battery Cells

In this study, we have investigated feasibility of using commercial explicit finite element code LS-DYNA on massively parallel super-computing cluster for accurate modeling of structural impact on battery cells. Physical and numerical lateral impact tests have been conducted on cylindrical cells using a flat rigid drop cart in a custom-built drop test apparatus. The main component of cylindrical cell, jellyroll, is a layered spiral structure which consists of thin layers of electrodes and separator. Two numerical approaches were considered: (1) homogenized model of the cell and (2) heterogeneous (full) 3-D cell model. In the first approach, the jellyroll was considered as a homogeneous material with an effective stress-strain curve obtained through experiments. In the second model, individual layers of anode, cathode and separator were accounted for in the model, leading to extremely complex and computationally expensive finite element model. To overcome limitations of desktop computers, high-performance computing (HPC) techniques on a HPC cluster were needed in order to get the results of transient simulations in a reasonable solution time. We have compared two HPC methods used for this model is shared memory parallel processing (SMP) and massively parallel processing (MPP). Both the homogeneous and the heterogeneous models were considered for parallel simulations utilizing different number of computational nodes and cores and the performance of these models was compared. This work brings us one step closer to accurate modeling of structural impact on the entire battery pack that consists of thousands of cells.

scholarly journals Simulation of pandemics in real cities: enhanced and accurate digital laboratories

2021 ◽  
Vol 477 (2245) ◽  
pp. 20200653
Author(s):  
A. Alexiadis ◽  
A. Albano ◽  
A. Rahmat ◽  
M. Yildiz ◽  
A. Kefal ◽  
...  
Keyword(s):  
Urban Environment ◽  
Time Resolution ◽  
High Performance ◽  
Adjustable Parameter ◽  
Massively Parallel ◽  
Statistical Features ◽  
Modelling Framework ◽  
Virtual Laboratories ◽  
Real Population ◽  
Performance Computing

This study develops a modelling framework for simulating the spread of infectious diseases within real cities. Digital copies of Birmingham (UK) and Bogotá (Colombia) are generated, reproducing their urban environment, infrastructure and population. The digital inhabitants have the same statistical features of the real population. Their motion is a combination of predictable trips (commute to work, school, etc.) and random walks (shopping, leisure, etc.). Millions of individuals, their encounters and the spread of the disease are simulated by means of high-performance computing and massively parallel algorithms for several months and a time resolution of 1 minute. Simulations accurately reproduce the COVID-19 data for Birmingham and Bogotá both before and during the lockdown. The model has only one adjustable parameter calculable in the early stages of the pandemic. Policymakers can use our digital cities as virtual laboratories for testing, predicting and comparing the effects of policies aimed at containing epidemics.

High Performance Computing: Analytical Aerodynamics for Automotive Vehicles

1999 ◽  
Author(s):  
Ronald H. Miller ◽  
Gary S. Strumolo ◽  
Evangelos Hytopoulos ◽  
Stephen A. Remondi ◽  
Samuel M. Watson
Keyword(s):  
Wind Tunnel ◽  
Parallel Algorithms ◽  
High Performance Computing ◽  
High Performance ◽  
Experimental Testing ◽  
Computer Architectures ◽  
Wind Tunnels ◽  
Cfd Simulations ◽  
Design Time ◽  
Performance Computing

Abstract High Performance Computing (HPC) represents a significant resource whereby automotive manufacturers can utilize analytical methodologies to reduce experimental testing and design time, resulting in lower costs and higher quality. Optimization of styling and aerodynamics requires multiple CFD simulations which have been enabled by the commercial availability of parallel algorithms, as well as enhancements in computer architectures. We have developed a Virtual Aerodynamic Wind Tunnel (VAWT) which uses PowerFLOW® and can simulate conditions similar to experimental wind tunnels. One key element of this methodology is the use of PowerFLOW. Two of the major attributes of PowerFLOW are its inherent parallelization and automeshing capabilities. In this paper, we will focus on the scalability and feasibility of PoweFLOW, which is essential for the optimization of styling and aerodynamics. Timing and scalability results on an Origin 2000 server are presented for a number of different configurations.

Introduction to Dataflow Computing

2021 ◽  
pp. 96-105
Author(s):  
Nenad Korolija ◽  
Jovan Popović ◽  
Miroslav M. Bojović
Keyword(s):  
Big Data ◽  
Power Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Programming Tools ◽  
Dataflow Computing ◽  
Dataflow Architecture ◽  
Significant Performance ◽  
Performance Gains ◽  
Performance Computing

This chapter presents the possibilities for obtaining significant performance gains based on advanced implementations of algorithms using the dataflow hardware. A framework built on top of the dataflow architecture that provides tools for advanced implementations is also described. In particular, the authors point out to the following issues of interest for accelerating algorithms: (1) the dataflow paradigm appears as suitable for executing certain set of algorithms for high performance computing, namely algorithms that work with big data, as well as algorithms that include a lot of repetitions of the same set of instructions; (2) dataflow architecture could be configured using appropriate programming tools that can define hardware by generating VHDL files; (3) besides accelerating algorithms, dataflow architecture also reduces power consumption, which is an important security factor with edge computing.

Power and Performance Management of GPUs Based Cluster

2012 ◽  
Vol 2 (4) ◽  
pp. 16-31 ◽  
Author(s):  
Yaser Jararweh ◽  
Salim Hariri
Keyword(s):  
Power Consumption ◽  
Performance Management ◽  
High Performance ◽  
Peak Performance ◽  
Gpu Cluster ◽  
Management Framework ◽  
High Productivity ◽  
Computing Industry ◽  
And Performance ◽  
Performance Computing

Power consumption in GPUs based cluster became the major obstacle in the adoption of high productivity GPU accelerators in the high performance computing industry. The power consumed by GPU chips represent about 75% of the total GPU based cluster power consumption. This is due to the fact that the GPU cards are often configured at peak performance, and consequently, they will be active all the time. In this paper, the authors present a holistic power and performance management framework that reduces power consumption of the GPU based cluster and maintains the system performance within an acceptable predefined threshold. The framework dynamically scales the GPU cluster to adapt to the variation of incoming workload’s requirements and increase the idleness of the of GPU devices, allowing them to transition to low-power state. The proposed power and performance management framework in GPU cluster demonstrated 46.3% power savings for GPU workload while maintaining the cluster performance. The overhead of the proposed framework is insignificant on the normal application\system operations and services.

Sign in / Sign up

Export Citation Format

Share Document